The association between food patterns and adiposity among Canadian children at risk of overweight
Bibliographic record
Abstract
Identifying food patterns related to obesity can provide information for health promotion in nutrition. Food patterns and their relation with obesity among Canadian children have not been reported to date. Our aim was to identify and describe food patterns associated with obesity in children at risk of overweight. Caucasian children (n = 630) with at least 1 obese biological parent recruited into the Quebec Adiposity and Lifestyle Investigation in Youth (QUALITY) cohort were studied in cross-sectional analyses. Measures of adiposity (body mass index (BMI), waist circumference, body fat mass percentage measured by dual-energy X-ray absorptiometry), screen time, physical activity (accelerometer over 7 days), and dietary intake (three 24-h food recalls) were collected. Factor analysis was used to identify food patterns. The relationships between food patterns and overweight were investigated in logistic and multiple linear regression models. Three food patterns were retained for analysis: traditional food (red meats, main dishes-soups, high-fat dairy products, tomato products, dressings, etc.); healthy food (low-fat dairy products, whole grains, legumes-nuts-seeds, fruits, vegetables); and fast food (sugar-sweetened beverages, fried potatoes, fried chicken, hamburgers-hot dogs-pizza, salty snacks). Higher scores on the fast food pattern were associated with overweight (BMI ≥ 85th percentile), and other measures of adiposity (BMI, waist circumference, body fat mass percentage) after adjustment for age, sex, physical activity, screen time, sleep time, family income, and mother's obesity (p < 0.05). Controlling for energy intake did not change these relationships. Our results provide further evidence of a link between fast food intake and obesity in children.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".